Call Transfer Support System

ABSTRACT

A computer retrieves a dialog information records of the active call of the first operator. The computer extracts features from the dialog information records. The computer determines a feature vector from the extracted features and determines a transfer probability value based on the feature vector and previous call transfers to the second operator.

BACKGROUND

The present invention relates, generally, to the field of computing, andmore particularly to transferring calls between operators of a callcenter based on a transfer prediction model.

Call centers in enterprises, typically, have a variety of membersranging from newly employed operators to veterans who have extensiveexperience in various fields. Majority of the call centers have adedicated software that allows operators to search for specific issuesor requests of the callers. However, in case of an issue, that a newlyemployed operator is unable to handle, the operator simply raises hishand and more experienced or senior operator intervenes to assist thecaller.

SUMMARY

According to one embodiment, a method, computer system, and computerprogram product for call transfer is provided. An embodiment may includea computer retrieving a dialog information records of the active call ofthe first operator. The embodiment may further extract features from thedialog information records. The embodiment may also determine a featurevector from the extracted features and a transfer probability valuebased on the feature vector and a previous call transfers to the secondoperator.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates an exemplary networked computer environment accordingto at least one embodiment;

FIG. 2A is an operational flowchart illustrating a learning process forthe call transfer support system, according to at least one embodiment;

FIG. 2B is an operational flowchart illustrating a prediction processfor the call transfer support system, according to at least oneembodiment;

FIG. 3 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 4 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 5 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. In the description, details ofwell-known features and techniques may be omitted to avoid unnecessarilyobscuring the presented embodiments.

Embodiments of the present invention relate to the field of computing,and more particularly to transferring calls based on a transferprediction model. The following described exemplary embodiments providea system, method, and program product to, among other things,automatically predict and perform a call transfer to a skilled operatorbased on a prediction model. Therefore, the present embodiment has thecapacity to improve the technical field of computerized call centerservices by determining a call prediction model based on previous calltransfers between the operators of the call center and thus improveperformance of the call center by effectively transferring calls to adesignated operator while avoiding repetitive questions and reducingresponse time to assist the caller.

As previously described, call centers in enterprises, typically have avariety of members ranging from newly employed operators to veterans whohave an extensive experience in various fields. A majority of callcenters have dedicated software that allows operators to search forspecific issues or caller requests. However, in the event of an issuethat a newly-employed operator is unable to handle, the operator simplyraises his hand and a more experienced or senior operator may interveneto assist the caller.

Typically, operators are evaluated by the number of calls assisted andare expected to deal with incoming calls without any assistance fromsenior or more experienced operators. In majority of call centers,operators that have difficulties assisting clients raise their hand andmore senior operators assist them by transferring the call to a skilledoperator. However, in many other instances the call has to betransferred to another operator not only because of lack of experiencebut because there are specific operators that are in charge for aspecific process, such as input data related to personal information.

As such, it may be advantageous to, among other things, implement asystem that evaluates operators based on their previous handling of thecalls including call duration, customer satisfaction and whether thecall was transferred to another operator and establishes a model fortransferring calls to a specific operator based on a feature vector.Then, based on the feature vector, the system may either recommend totransfer the call to a specific operator using a graphical userinterface (GUI) of the call center or automatically transfer the call,thus improving the efficiency of the call center and increasing customersatisfaction.

Feature vector is a multidimensional vector where each value representsextracted from the dialog text features and temporal features. Forexample, feature vector may be in a flag (Boolean) format. Text featuresare features that may be extracted from the text itself, such as whetherthe customer asked a question, whether a discount was requested, or acustomer requested that contact information or billing needs to beupdated. Temporal features are typically time related features that arecapable of being extracted through time analysis of the dialog, such asconversation duration, silent time of the operator, overall time of thecommunication, etc. Feature vector may be determined either by matchingwords and symbols in the converted to text conversation or by using atrained deep neural network.

According to one embodiment, a call transfer program may extract andanalyze the dialog between each operator and their customer, establish atransfer prediction model by transferring each dialog to a featurevector that may be compared to a call transfer requirements settingsand, by determining the probability of transfer value, either displaythe probability of transfer of the current call or transfer the call tothe most appropriate available operator. In another embodiment, the calltransfer program may analyze the call between a customer and a computeroperator and determine an appropriate time to transfer the call to ahuman operator. In further embodiments, the transfer prediction modelmay be established and updated for each available operator.

According to an example embodiment, the transfer prediction model may bea mathematical expression that evaluates the feature vectors and thetransfer information of each call and creates a set of rules or atransfer probability value that may be used to determine whether thecurrent call should be transferred and the target operator that shouldanswer the call based on a previously recorded dialogs and the operatorsthat ended up receiving the call in the past. For example, the transferprediction model may have a set of weights for each value of the featurevector that may be summarized into the transfer probability value and,if the value is above a predetermined threshold, the call should betransferred to an operator that has a highest transfer probability valuethat is above the predetermined threshold.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The following described exemplary embodiments provide a system, method,and program product to create a call transfer prediction model from theprevious dialogs between operators and customers and determine whentiming dictates transferring a call to a skilled operator is necessary.

Referring to FIG. 1, an exemplary networked computer environment 100 isdepicted, according to at least one embodiment. The networked computerenvironment 100 may include client computing device 102 and a server 112interconnected via a communication network 114. According to at leastone implementation, the networked computer environment 100 may include aplurality of client computing devices 102 and servers 112, of which onlyone of each is shown for illustrative brevity.

The communication network 114 may include various types of communicationnetworks, such as a wide area network (WAN), local area network (LAN), atelecommunication network, a wireless network, a public switched networkand/or a satellite network. The communication network 114 may includeconnections, such as wire, wireless communication links, or fiber opticcables. It may be appreciated that FIG. 1 provides only an illustrationof one implementation and does not imply any limitations with regard tothe environments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

Client computing device 102 may include a processor 104 and a datastorage device 106 that is enabled to host and run a software program108 and a call transfer program 110A and communicate with the server 112via the communication network 114, in accordance with one embodiment ofthe invention. Client computing device 102 may be, for example, a mobiledevice, a telephone, a personal digital assistant, a netbook, a laptopcomputer, a tablet computer, a desktop computer, or any type ofcomputing device capable of running a program and accessing a network.As will be discussed with reference to FIG. 3, the client computingdevice 102 may include internal components 302 a and external components304 a, respectively.

The server computer 112 may be a laptop computer, netbook computer,personal computer (PC), a desktop computer, or any programmableelectronic device or any network of programmable electronic devicescapable of hosting and running a call transfer program 110B and storingand accessing data from storage 116 and communicating with the clientcomputing device 102 via the communication network 114, in accordancewith embodiments of the invention. Storage 116 may be a tangible storagedevice 330 configured to store digital data such as voice calls inrecords 118, and logs, feature vectors and any data required for theoperation of call transfer program 110B in dialog information 120. Aswill be discussed with reference to FIG. 3, the server computer 112 mayinclude internal components 302 b and external components 304 b,respectively. The server 112 may also operate in a cloud computingservice model, such as Software as a Service (SaaS), Platform as aService (PaaS), or Infrastructure as a Service (IaaS). The server 112may also be located in a cloud computing deployment model, such as aprivate cloud, community cloud, public cloud, or hybrid cloud.

According to the present embodiment, the call transfer program 110A,110B may be a program capable of analyzing previous calls, determining afeature vector from each call and creating and updating the transferprediction model that is used to determine the probability of calltransfer to a corresponding available operator. The call transfer methodis explained in further detail below with respect to FIGS. 2A and 2B.

Referring now to FIG. 2A, an operational flowchart illustrating alearning process for the call transfer support process 200A is depictedaccording to at least one embodiment. At 202, the call transfer program110A, 110B converts recorded dialog to text with time stamps usingspeech-to-text technology. According to an example embodiment, calltransfer program 110A, 110B may access stored recordings of previousconversations with customers from a database, such as records 118, andconvert the recordings to text logs using natural language processing(NLP) techniques. NLP is a field of computer science, artificialintelligence, and computational linguistics related to the interactionsbetween computers and human natural languages, such as programmingcomputers to process natural language and convert it from voice to text.According to an example embodiment, call transfer program 110A, 110B mayconvert the voice recordings to a log where each question of thecustomer and operator answer is accompanied by the corresponding timestamp.

Then, at 204, the call transfer program 110A, 110B determines whetherunlearned dialog information exists for the recording. For example, calltransfer program 110A, 110B may check whether the feature vector wasgenerated for the corresponding dialog. If the call transfer program110A, 110B determines that the feature vector does not exist (i.e., thatan unlearned dialog information exists) (step 204, “YES” branch), thecall transfer program 110A, 110B may continue to step 206 to extractfeatures from the converted dialog. If the call transfer program 110A,110B determines that there are no unlearned dialog information (i.e.,the feature vector exists for the dialog) (step 204, “NO” branch), thecall transfer program 110A, 110B may continue to step 212 to updatetransfer prediction model.

Next, at 206, the call transfer program 110A, 110B extracts featuresfrom the converted dialog. According to an example embodiment, calltransfer program 110A, 110B may extract text features and temporalfeatures from the converted dialog in a flag (Boolean) format. Textfeatures may be features that may be extracted from the text itself,such as whether the customer asked a question, whether a discount wasrequested, or a customer requested that contact information or billingneeds to be updated. Temporal features may be features that are capableof being extracted through time analysis, such as conversation timeduration, silent time of the operator, etc. According to an exampleembodiment, the call transfer program 110A, 110B may extract textfeatures using a simple search for specific words or symbols in theconverted dialog while temporal features may be calculated from timedata.

For example, consider the following dialog:

TABLE 1 Speaker Time Speech Operator A 14:00:05 . . . your inquiry isabout the insurance for your wife. Customer 14:00:45 Yes, I heard we canget a family discount. Operator A 14:00:51 Discount. . . , hold onplease. I will check the insurance information. : : : 14:33:52<Transfer> (From Operator A→Operator Z)

To continue the previous example, call transfer program 110A, 110B maydetermine call statistics using NLP, voice analysis, text analysis,and/or statistical analysis. For example, the call transfer program110A, 110B may determine, that no questions were asked, that “family”and “discount” words were used during the conversation and that thesilent time was 25.2 seconds while the total call duration was 98seconds.

Next, at 208, the call transfer program 110A, 110B determines a featurevector. According to an example embodiment, the call transfer program110A, 110B may generate a feature vector in a flag (Boolean) format. Tocontinue the previous example, call transfer program 110A, 110B maygenerate the following feature vector from the values of the extractedfeatures:

TABLE 2 Dimension (Features) Value Question^(*) 0 (no) Family^(*) 1(yes) Discount^(*) 1 (yes) : : Silent time 25.2 Duration 98.0 : :

In another embodiment, the call transfer program 110A, 110B may consideradditional elements, such as search count that the operator performedduring the call, search result click count of the operator during thecall, a career of the operator, and whether the topic is new to theoperator.

In a further embodiment, the call transfer program 110A, 110B mayutilize word embedding or a trained neural network to transfer thedialog into a set of vectors that afterwards may determine dimensionsusing a trained neural network. Word embedding is typically a collectivename for a set of language modeling and feature learning techniques inNLP where words or phrases from a text are mapped to vectors or a set ofcoordinates of real numbers. A neural network is a computational modelin computer science that is based on a collection of neural units. Eachneural unit is an artificial neuron that may be connected with otherneural units to create a neural network. The neural network may then betrained to find a solution to a problem where a traditional computerprogram fails, such as NLP of a text or word embedding.

In another embodiment, Bayesian inference models may be introduced thatare based on a method of statistical inference of a probability wherethe Bayes theorem is used to update the probability each time relatedinformation becomes available.

In further embodiments, the call transfer program 110A, 110B may utilizea logistic regression if the feature vector elements are independentlyeffective and deep neural network when considering combinatorialrelationships between elements of the feature vector.

Next, at 210, the call transfer program 110A, 110B transfers the featurevector with a transfer information to storage. According to an exampleembodiment, call transfer program 110A, 110B may store the featurevector and the transfer information in the dialog information 120.Transfer information may be any additional information related to theanalyzed call such as whether the call was transferred, a name or aphone of the operator where the call was transferred or otheridentification of the call transfer.

Next, at 212, the call transfer program 110A, 110B updates the transferprediction model. According to an example embodiment, call transferprogram 110A, 110B may create and update the transfer prediction modelthat evaluates the feature vector and the transfer information to createa set of rules or a probability as to whether the call should betransferred and the target of the transfer based on a previoustransfers. As previously mentioned, the model may have a set of weightsfor each value of the feature vector that may be updated based on thetransfer. For example, the values may be weighted according to thefollowing table:

Dimension Value Question 0.01 Product −0.03 Discount 0.17 : : Silenttime 0.59 Duration 0.2 : :

In further embodiments, the call transfer program 110A, 110B may updatethe weights based on the summarized value (i.e. the transfer probabilityvalue) such as that the similar feature vector (where the feature vectorvalues having same or similar values) would trigger transfer to the sameoperator.

Referring now to FIG. 2B, an operational flowchart illustrating aprediction process for the call transfer support process 200B isdepicted according to at least one embodiment. At 214, the call transferprogram 110A, 110B retrieves the dialog information records of activecalls. According to an example embodiment, the call transfer program110A, 110B may use NLP techniques to convert an ongoing call in realtime from speech to text with a corresponding time stamp representingwhen each statement of the conversation started. For example, Table 1above represents statements of the caller and operator with thecorresponding timestamp.

Then, at 216, the call transfer program 110A, 110B determines whether atransfer probability was reached for the current call. According to anexample embodiment, call transfer program 110A, 110B may make adetermination based on whether a transfer probability value is above atransfer threshold value that is set by the user. In another embodiment,call transfer program 110A, 110B may make a determination based on anoverall time of the current call, or a silent time of the operator. In afurther embodiment, the call transfer program 110A, 110B may convert theextracted dialog into a preliminary feature vector multiplied by theweighted value of the transfer prediction model that may be compared toa specific threshold value. If the call transfer program 110A, 110Bdetermines that the transfer probability was reached (i.e. that thetransfer probability value is above a transfer threshold value) (step216, “YES” branch), the call transfer program 110A, 110B may continue tostep 218 to extract features from the conversation dialog. If the calltransfer program 110A, 110B determines that no unprocessed dialoginformation exists (step 216, “NO” branch), the call transfer program110A, 110B may continue to step 224 to transfer the call based on thetransfer probability.

Next, at 218, the call transfer program 110A, 110B extracts featuresfrom the converted dialog. According to an example embodiment, calltransfer program 110A, 110B may extract text features and temporalfeatures from the converted dialog in a flag (Boolean) format. Forexample, text features may be illustrated by the first three features inTable 2 while temporal features may be the bottom two features in Table2. According to an example embodiment, call transfer program 110A, 110Bmay extract text features using similar techniques as in step 206 (SeeFIG. 2A), such as by natural language search for specific words orsymbols in the converted dialog, while temporal features may becalculated from time data, such as a time log of the convertedconversation.

Next, at 220, the call transfer program 110A, 110B determines a featurevector. According to an example embodiment, call transfer program 110A,110B may generate a feature vector in a flag (Boolean) format, similarto step 208 (See FIG. 2A). As previously mentioned, call transferprogram 110A, 110B may determine call statistics using NLP, voiceanalysis, text analysis, and/or statistical analysis. For example, thecall transfer program 110A, 110B may determine, whether questions wereasked by the caller, that predetermined key words were used, such as“family”, “discount”, “transfer”, during the call and assign Booleanflags at the specific dimension of the feature vector. In addition, calltransfer program 110A, 110B may analyze time stamps during the call,such as the overall call duration, time duration between the caller'squestion, and operator's answer.

In another embodiment, call transfer program 110A, 110B may utilize wordembedding or a trained neural network to transfer the dialog into a setof vectors that, afterwards, may determine dimensions using the trainedneural network. In yet another embodiment, Bayesian inference models maybe introduced that are based on a method of statistical inference of aprobability where the Bayes theorem is used to update the probabilityeach time related information becomes available.

Next, at 222, the call transfer program 110A, 110B determines transferprobability. According to an example embodiment, the call transferprogram 110A, 110B may summarize the multiplication between the featurevector values with the corresponding weights that the call transferprogram 110A, 110B determined during the learning process and stored indialog information 120. The summarized value may then be converted to atransfer probability value that, afterwards, may be compared to a calltransfer threshold value in order to determine whether the call transferprogram 110A, 110B should transfer the call to another operator.According to an example embodiment, the call transfer program 110A, 110Bmay determine the transfer probability value by normalizing thesummarized value. In another embodiment, the call transfer program 110A,110B may determine the transfer probability value using a trained deepneural network or Bayesian inference model. In further embodiments, thecall transfer program 110A, 110B may determine the transfer probabilityvalue for each available operator.

Next, at 224, if the call transfer program 110A, 110B determines alldialog information has been processed, the call transfer program 110A,110B transfers the call based on the transfer probability. According toan example embodiment, call transfer program 110A, 110B may display theactive calls to one or more operators using a GUI where the transferprobability value, or its percentage representation, is shown indescending order for each call. An available operator may review thetransfer probability and the call transcript and request the transfer ofthe call to his phone through a user selection on the GUI. In anotherembodiment, call transfer program 110A, 110B may display the transferprobability value, or its percentage representation, based on a personaltransfer prediction model calculated for each available operator. In afurther embodiment, call transfer program 110A, 110B may transfer thecall to the available operator that has the highest transfer probabilityvalue, or its highest percentage representation, when the value iscalculated for each available operator individually.

It may be appreciated that FIGS. 2A-2B provides only an illustration ofone implementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 3 is a block diagram 300 of internal and external components of theclient computing device 102 and the server 112 depicted in FIG. 1 inaccordance with an embodiment of the present invention. It should beappreciated that FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The data processing system 302, 304 is representative of any electronicdevice capable of executing machine-readable program instructions. Thedata processing system 302, 304 may be representative of a smart phone,a computer system, PDA, or other electronic devices. Examples ofcomputing systems, environments, and/or configurations that mayrepresented by the data processing system 302, 304 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, network PCs, minicomputersystems, and distributed cloud computing environments that include anyof the above systems or devices.

The client computing device 102 and the server 112 may includerespective sets of internal components 302 a,b and external components304 a,b illustrated in FIG. 3. Each of the sets of internal components302 include one or more processors 320, one or more computer-readableRAMs 322, and one or more computer-readable ROMs 324 on one or morebuses 326, and one or more operating systems 328 and one or morecomputer-readable tangible storage devices 330. The one or moreoperating systems 328, the software program 108 and the call transferprogram 110A in the client computing device 102, and the call transferprogram 110B in the server 112 are stored on one or more of therespective computer-readable tangible storage devices 330 for executionby one or more of the respective processors 320 via one or more of therespective RAMs 322 (which typically include cache memory). In theembodiment illustrated in FIG. 3, each of the computer-readable tangiblestorage devices 330 is a magnetic disk storage device of an internalhard drive. Alternatively, each of the computer-readable tangiblestorage devices 330 is a semiconductor storage device such as ROM 324,EPROM, flash memory or any other computer-readable tangible storagedevice that can store a computer program and digital information.

Each set of internal components 302 a,b also includes a R/W drive orinterface 332 to read from and write to one or more portablecomputer-readable tangible storage devices 338 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the cognitivescreen protection program 110A, 110B, can be stored on one or more ofthe respective portable computer-readable tangible storage devices 338,read via the respective R/W drive or interface 332, and loaded into therespective hard drive 330.

Each set of internal components 302 a,b also includes network adaptersor interfaces 336 such as a TCP/IP adapter cards, wireless Wi-Fiinterface cards, or 3G or 4G wireless interface cards or other wired orwireless communication links. The software program 108 and the calltransfer program 110A in the client computing device 102 and the calltransfer program 110B in the server 112 can be downloaded to the clientcomputing device 102 and the server 112 from an external computer via anetwork (for example, the Internet, a local area network or other, widearea network) and respective network adapters or interfaces 336. Fromthe network adapters or interfaces 336, the software program 108 and thecall transfer program 110A in the client computing device 102 and thecall transfer program 110B in the server 112 are loaded into therespective hard drive 330. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 304 a,b can include a computerdisplay monitor 344, a keyboard 342, and a computer mouse 334. Externalcomponents 304 a,b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 302 a,b also includes device drivers 340to interface to computer display monitor 344, keyboard 342, and computermouse 334. The device drivers 340, R/W drive or interface 332, andnetwork adapter or interface 336 comprise hardware and software (storedin storage device 330 and/or ROM 324).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 100 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 100 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 4 are intended to be illustrative only and that computing nodes100 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 5, a set of functional abstraction layers 500provided by cloud computing environment 50 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 5 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and call transfer prediction 96. Calltransfer prediction 96 may relate to analyzing incoming calls of acalling center and by transferring each call into a feature vectordetermining whether the call should be transferred to a differentoperator.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A processor-implemented method for transferringan active call, the method comprising: retrieving dialog informationrecords; extracting features from the dialog information records;determining a feature vector from the extracted features; determining atransfer probability value based on the feature vector and previous calltransfers to a second operator, wherein the transfer probability valueis determined by summarizing each dimension of the feature vectormultiplied by a corresponding weight to each dimension; and transferringthe active call to the second operator based on determining the transferprobability value is above a threshold value.
 2. The method of claim 1,further comprising: transferring the active call to the second operatorbased on determining the transfer probability value is above a thresholdvalue.
 3. The method of claim 1, wherein determining the transferprobability value based on the feature vector and previous calltransfers to the second operator comprises: extracting features from thedialog information records of each previous call to the second operator;determining a feature vector from the extracted features; determining acorresponding weight to the transfer probability value by a transferprediction model; updating the transfer prediction model, wherein thetransfer prediction model comprises a set of weights updated for eachvalue of the feature vector; and determining the transfer probabilityvalue based on applying the feature vector of the active call to thetransfer prediction model.
 4. The method of claim 3, wherein updatingthe transfer prediction model is by a logistic regression when thefeature vector has elements that are independently effective and by adeep neural network when the elements have combinatorial relationships.5. The method of claim 1, wherein extracting features from the dialoginformation records is by deep neural network.
 6. The method of claim 1,wherein the dialog information records are converted usingtext-to-speech dialogs with a corresponding timestamp.
 7. The method ofclaim 6, wherein the feature vector comprises text features and temporalfeatures, wherein the text features are extracted from the dialoginformation records and temporal features are extracted from a time log.8. A computer system for transferring an active call, the computersystem comprising: one or more processors, one or more computer-readablememories, one or more computer-readable tangible storage medium, andprogram instructions stored on at least one of the one or more tangiblestorage medium for execution by at least one of the one or moreprocessors via at least one of the one or more memories, wherein thecomputer system is capable of performing a method comprising: retrievingdialog information records; extracting features from the dialoginformation records; determining a feature vector from the extractedfeatures; determining a transfer probability value based on the featurevector and previous call transfers to a second operator, wherein thetransfer probability value is determined by summarizing each dimensionof the feature vector multiplied by a corresponding weight to eachdimension; and transferring the active call to the second operator basedon determining the transfer probability value is above a thresholdvalue.
 9. The computer system of claim 8, further comprising:transferring the active call to the second operator based on determiningthe transfer probability value is above a threshold value.
 10. Thecomputer system of claim 8, wherein determining the transfer probabilityvalue based on the feature vector and previous call transfers to thesecond operator comprises: extracting features from the dialoginformation records of each previous call to the second operator;determining a feature vector from the extracted features; determining acorresponding weight to the transfer probability value by a transferprediction model; updating the transfer prediction model, wherein thetransfer prediction model comprises a set of weights updated for eachvalue of the feature vector; and determining the transfer probabilityvalue based on applying the feature vector of the active call to thetransfer prediction model.
 11. The computer system of claim 10, whereinupdating the transfer prediction model is by a logistic regression whenthe feature vector has elements that are independently effective and bya deep neural network when the elements have combinatorialrelationships.
 12. The computer system of claim 8, wherein extractingfeatures from the dialog information records is by deep neural network.13. The computer system of claim 8, wherein the dialog informationrecords are converted using text-to-speech dialogs with a correspondingtimestamp.
 14. The computer system of claim 13, wherein the featurevector comprises text features and temporal features, wherein the textfeatures are extracted from the dialog information records and temporalfeatures are extracted from a time log.
 15. A computer program productfor transferring an active call, the computer program productcomprising: one or more computer-readable tangible storage medium andprogram instructions stored on at least one of the one or more tangiblestorage medium, the program instructions executable by a processor, theprogram instructions comprising: program instructions to retrieve dialoginformation records; program instructions to extract features from thedialog information records; program instructions to determine a featurevector from the extracted features; program instructions to determine atransfer probability value based on the feature vector and previous calltransfers to a second operator, wherein the transfer probability valueis determined by program instructions to summarize each dimension of thefeature vector multiplied by a corresponding weight to each dimension;and program instructions to transfer the active call to the secondoperator based on determining the transfer probability value is above athreshold value.
 16. The computer program product of claim 15, furthercomprising: program instructions to transfer the active call to thesecond operator based on determining the transfer probability value isabove a threshold value.
 17. The computer program product of claim 15,wherein program instructions to determine the transfer probability valuebased on the feature vector and previous call transfers to the secondoperator comprises: program instructions to extract features from thedialog information records of each previous call to the second operator;program instructions to determine a feature vector from the extractedfeatures; program instructions to determine a corresponding weight tothe transfer probability value by a transfer prediction model; programinstructions to update the transfer prediction model, wherein thetransfer prediction model comprises a set of weights updated for eachvalue of the feature vector; and program instructions to determine thetransfer probability value based on applying the feature vector of theactive call to the transfer prediction model.
 18. The computer programproduct of claim 17, wherein program instructions to update the transferprediction model is by a logistic regression when the feature vector haselements that are independently effective and by a deep neural networkwhen the elements have combinatorial relationships.
 19. The computerprogram product of claim 15, wherein program instructions to extractfeatures from the dialog information records is by deep neural network.20. The computer program product of claim 15, wherein the dialoginformation records are converted using text-to-speech dialogs with acorresponding timestamp.